With increasing availability, the use of healthcare databases as complementary data source for drug safety signal detection has been explored to circumvent the limitations inherent in spontaneous reporting. Areas covered: To review the methods proposed for safety signal detection in healthcare databases and their performance. Expert opinion: Fifteen different data mining methods were identified. They are based on disproportionality analysis, traditional pharmacoepidemiological designs (e.g. self-controlled designs), sequence symmetry analysis (SSA), sequential statistical testing, temporal association rules, supervised machine learning (SML), and the tree-based scan statistic. When considering the performance of these methods, the self-controlled designs, the SSA, and the SML seemed the most interesting approaches. In the perspective of routine signal detection from healthcare databases, pragmatic aspects such as the need for stakeholders to understand the method in order to be confident in the results must be considered. From this point of view, the SSA could appear as the most suitable method for signal detection in healthcare databases owing to its simple principle and its ability to provide a risk estimate. However, further developments, such as automated prioritization, are needed to help stakeholders handle the multiplicity of signals.

Several genetic and nongenetic benefits have been proposed to explain multiple mating (polyandry) in animals, to compensate for costs associated with obtaining additional mates. The most prominent hypotheses stress the benefits of increased genetic diversity. In social insects, queens of most species mate only once or have effective mating frequencies close to one. Yet, in a few species of ants, bees, and wasps, polyandry is the rule. In these species, colonies are usually headed by a single queen, whereas multiple queening adds diversity in several of the remaining species, especially in ants. Here we investigated mating frequency, inbreeding and relatedness between the queens and their mates in the polygynous ant Plagiolepis pygmaea, and the effect of polyandry on the genetic diversity as a function of the effective population size of individual colonies. Our results show that polyandry occurs frequently in the species. However, queens are frequently inseminated by close relatives, and additional sires add little genetic diversity among offspring of individual queens. In addition, the increase in diversity at the colony level is only marginal. Hence, contrary to established notions, polyandry in P. pygmaea seems not to be driven by substantial benefits of genetic diversity. Nonetheless, very small or as yet unidentified genetic benefits to one party (males, workers, queens) in conjunction with low costs of mating may favor polyandry. Alternatively, nongenetic factors, such as convenience polyandry, may be more important than genetic factors in promoting polyandry in P. pygmaea.

Background Nonsteroidal anti-inflammatory drugs (NSAIDs) have been discouraged for the treatment of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections, fearing that they could increase the risk of infection or the severity of SARS-CoV-2. Methods Original studies providing information on exposure to NSAIDs and coronavirus disease 2019 (COVID-19) outcomes were retrieved and were included in a descriptive analysis and a meta-analysis with Cochrane Revue Manager (REVMAN 5.4), using inverse variance odds ratio (OR) with random- or fixed-effects models. Results Of 92,853 papers mentioning COVID-19, 266 mentioned NSAIDs and 61 mentioned ibuprofen; 19 papers had analysable data. Three papers described NSAID exposure and the risk of SARS-CoV-2 positivity, five papers described the risk of hospital admission in positive patients, 10 papers described death, and six papers described severe composite outcomes. Five papers studied exposure to ibuprofen and death. Using random-effects models, there was no excess risk of SARS-CoV-2 positivity (OR 0.86, 95% confidence interval [CI] 0.71–1.05). In SARS-CoV-2-positive patients, exposure to NSAIDs was not associated with excess risk of hospital admission (OR 0.90, 95% CI 0.80–1.17), death (OR 0.88, 95% CI 0.80–0.98), or severe outcomes (OR 1.14, 95% CI 0.90–1.44). With ibuprofen, there was no increased risk of death (OR 0.94, 95% CI 0.78–1.13). Using a fixed-effect model did not modify the results, nor did the sensitivity analyses. Conclusion The theoretical risks of NSAIDs or ibuprofen in SARS-CoV-2 infection are not confirmed by observational data. Supplementary Information The online version contains supplementary material available at 10.1007/s40264-021-01089-5.